Improvement in Injection Molding:

From Tribal Knowledge To AI-Standard Downtime Classification

Driving a 20.6% Availability Improvement in Injection Molding.

A world-class global electronics and plastics manufacturer partnered with Arch Systems to modernize performance management across a 60+ machine injection molding operation for healthcare devices.

By enabling AI-based downtime classification, real-time process visibility, and proactive maintenance actions, the factory achieved a 20.6% improvement in availability in just five weeks, unlocking over $1.2M in annual savings with an ROI of more than 15x, and triggering a global expansion plan to more than 750 IMMs across three continents.

Case study - 20% Availability Improvement in Injection Molding

HIGHLIGHTS:

improvement in
equipment availability

15x ROI

realized in under
five weeks

$1.2M

annualized savings unlocked
with initial Arch rollout

750 injection molding machines in planned global expansion

The Challenge

Despite a highly automated environment, the site lacked common visibility into machine status, downtime causes, and early-warning signs for process degradation.
Key operational obstacles included:

More than 60 IMM from different vendors and generations
250+ downtime reasons inside 10 categories, difficult to navigate during production
High dependency on expert knowledge for downtime classification
Manual interpretation of maintenance and quality conditions
Slow escalation of emerging performance and quality issues

As a result, even experienced technicians spent valuable time searching for correct downtime reasons, while junior operators lacked the training and context to classify properly. Downtime data lacked consistency, limiting true OEE and masking actionable availability opportunities.

The Arch Approach

Arch deployed its Factory Intelligence Platform with Arch AI-powered Downtime Labeling, SPC-driven insights, and automated maintenance actions, designed specifically for heterogeneous machine fleets and complex operational taxonomies.
Arch delivered transformation across four dimensions:

AI-Powered Downtime Classification: Operators simply describe downtime using natural, local language speech. Arch AI automatically interprets the event, standardizes the reason, and assigns the proper global category without searching through 250+ possible choices. This eliminated years of tribal-knowledge dependency and enabled reliable global benchmarking. Even senior SMEs preferred AI auto-labeling due to speed, accuracy, and reduced decision fatigue.
Real-Time Machine Status & Performance: Every IMM (regardless of age or vendor) was connected into a unified global view enabling uptime/availability status, cycle time variation, process trend shifts, and quality risk signals. Machine-to-enterprise visibility replaced isolated data silos.
Intelligent Action Workflows: Arch GLO React and Arch AI automatically triggered process investigations, maintenance checks, quality confirmations, and preventive actions. All actions digitally documented with accountability and traceability.
SPC + Process Trend Deviations: Leveraging real time SPC for injection molding, Arch detected early deviations, drift, tooling degradation, cycle-time performance loss, and quality escape risk. Teams could now respond before downtime or scrap accelerated.IMM critical parameters were deeply analyzed, assessed, and integrated into ArchFX GLO Action Manager Parameter Deviation module with SPC, enabling maintenance, toolroom technicians, engineers, and SMEs with real-time and historical parametric visibility.
Arch automatically generates alerts with specific data correlations to prevent process deviations and quality issues and provides guided actions to prevent or correct these events.

Integrated Quality Knowledge

Visual PLM quality documentation was presented directly to operators and technicians, enabling standardized classification, faster decision making, and consistent corrective actions.

Key Outcomes

20.6% Availability Improvement in only 5 weeks

Arch enabled data-driven action improving the OEE Availability component by 20.6% relative to baseline across 30 pilot IMMs.
This translated into:

>$1.2M annual savings
>15x ROI
Achieved within the first proof-of-concept period of 5 weeks

Global Scaling from a single site to a global transformation strategy

Rapid results accelerated enterprise adoption. Based on validated ROI and operational stability, the manufacturer initiated expansion across:

750+ IMMs
3 continents: America, Europe, and Asia
Standardized global downtime taxonomy AI-assisted operators everywhere
Projected annual savings exceed: $12.5M+ globally.

Strategic Lessons

AI enabled relief of the operational burden that historically blocked OEE improvement

Significatively reduced tribal knowledge dependency unleashes scale
AI-powered labeling standardizes global KPIs
SPC enables prevention instead of reaction
Unified visibility transforms decision-making speed
Natural language lowers adoption barriers for operators
Standard data accelerates enterprise ROI

When operators are empowered with intuitive AI tools, and leadership receives trusted, real-time operational KPIs, availability improvement becomes predictable, repeatable, and globally scalable.